Alexander V. Matus

and 3 more

Spaceborne lidar offers unique advantages for improving global estimates of fine particulate matter PM2.5, traditionally limited by critical data gaps in the vertical dimension. Here, we present a new method to retrieve PM2.5 relying on ensembles on aerosol extinction available within the GEOS Aerosol Data Assimilation. This study uses 1064-nm backscatter lidar data from the NASA Cloud-Aerosol Transport System (CATS) and model priors from the GEOS model. First, we developed a 1-D ensemble-based variational technique (1-D EnsVar) to perform vertically-resolved retrievals of speciated aerosol extinction and surface PM2.5. Next, we evaluated the performance of 1-D EnsVar retrievals of PM2.5 and extinction through an independent validation using measurements from spaceborne, airborne, and ground-based platforms. This approach overcomes traditional limitations by leveraging the strengths of complementary vertical aerosol information from CATS and GEOS to better resolve speciated aerosol optical properties and mass. With the advantage of active remote sensing, this approach is fully capable of performing aerosol retrievals during both daytime and nighttime scenes. Given the unique capability of CATS to process vertical profile data in near real-time, this work demonstrates the powerful utility of spaceborne lidar for improving air quality forecasting. While this pilot study is not yet performed within a cycling data assimilation system, the algorithm developed here can easily be integrated in such systems. Results presented may be useful in other applications as well, including the validation of aerosol transport models, improving passive satellite retrievals of PM2.5, and developing data assimilation techniques for future lidar platforms.

Susan M. O'Neill

and 39 more

Biomass burning has shaped many of the ecosystems of the planet and for millennia humans have used it as a tool to manage the environment. When widespread fires occur, the health and daily lives of millions of people can be affected by the smoke, often at unhealthy to hazardous levels leading to a range of short-term and long-term health consequences such as respiratory issues, cardiovascular issues, and mortality. It is critical to adequately represent and include smoke and its consequences in atmospheric modeling systems to meet needs such as addressing the global climate carbon budget and informing and protecting the public during smoke episodes. Many scientific and technical challenges are associated with modeling the complex phenomenon of smoke. Variability in fire emissions estimates has an order of magnitude level of uncertainty, depending upon vegetation type, natural fuel heterogeneity, and fuel combustion processes. Quantifying fire emissions also vary from ground/vegetation-based methods to those based on remotely sensed fire radiative power data. These emission estimates are input into dispersion and air quality modeling systems, where their vertical allocation associated with plume rise, and temporal release parameterizations influence transport patterns, and, in turn affect chemical transformation and interaction with other sources. These processes lend another order of magnitude of variability to the downwind estimates of trace gases and aerosol concentrations. This chapter profiles many of the global and regional smoke prediction systems currently operational or quasi-operational in real time or near-real time. It is not an exhaustive list of systems, but rather is a profile of many of the systems in use to give examples of the creativity and complexity needed to simulate the phenomenon of smoke. This chapter, and the systems described, reflect the needs of different agencies and regions, where the various systems are tailored to the best available science to address challenges of a region. Smoke forecasting requirements range from warning and informing the public about potential smoke impacts to planning burn activities for hazard reduction or resource benefit. Different agencies also have different mandates, and the lines blur between the missions of quasi-operational organizations (e.g. research institutions) and agencies with operational mandates. The global smoke prediction systems are advanced, and many are self-organizing into a powerful ensemble, as discussed in section 2. Regional and national systems are being developed independently and are discussed in sections 3-5 for Europe (11 systems), North America (7 systems), and Australia (3 systems). Finally, the World Meteorological Organization (WMO) effort (section 6) is bringing together global and regional systems and building the Vegetation Fire and Smoke Pollution Advisory and Assessment Systems (VFSP-WAS) to support countries with smoke issues and who lack resources.